SFB 876: Providing Information by Resource-Constrained Data Analysis

SFB 876研究中心汇集了数据分析的研究领域（大数据分析、数据挖掘、机器学习、统计学）和网络物理系统（嵌入式系统），并增强了他们的方法，使来自分布式，动态的大量数据的信息变得随时随地可用。资源限制来自处理大容量，大维度，或高速数据的方式。数据量和计算资源之间的关系决定了算法的可扩展性。对于小型、无处不在的设备，资源约束是显而易见的，但对于超大型、高维、动态数据的大型计算设施，资源约束也是显而易见的。 虽然运行时间是一个标准的考虑因素，研究社区的注意力进一步的资源，如内存，能源和通信已成为最近的研究重点。数据分析算法研究了几种编程范式（数据流，MapReduce）和执行平台（FPGA，GPU），考虑到他们的资源需求。建立资源模型是为了正确评估资源，即能源消耗或通信。在这些方面，SFB 876是一个先驱。SFB 876中的项目协调如下。B项目中的挑战源于本地的、移动的、无处不在的系统，如分布式嵌入式系统或小型病毒扫描程序。C项目中的资源限制来自于高维、动态和数据量，例如，由天文和粒子物理产生的数据。A领域的总体项目包括其他两个支柱。

The research center SFB 876 brings together the research fields of data analysis (Big Data Analytics, Data Mining, Machine Learning, Statistics) and Cyber-Physical Systems (Embedded Systems) and enhances their methods such that information from distributed, dynamic masses of data becomes available anytime and anywhere.Resource constraints arise from the need to handle large-volume, large-dimensional, or high-velocity data in an efficient way. The relation between the data size and the computing resources determines the necessary scalability of algorithms. For small, ubiquitous devices, the resource constraints are directly apparent, but they hold as well for large computing facilities in the case of extremely large, high-dimensional, dynamic data. Whereas runtime is a standard consideration, attention of the research community for further resources such as memory, energy, and communication has come to focus in recent research. Data analysis algorithms are investigated for several programming paradigms (data streams, MapReduce) and execution platforms (FPGA, GPU), taking their resource requirements into account. Resource models are established for the correct evaluation of resources, namely energy consumption or communication. In these perspectives, SFB 876 is a pioneer.Projects in SFB 876 are coordinated as follows. The challenges in the B projects stem from local, mobile, ubiquitous systems such as distributed embedded systems or small virus scanners. Resource constraints in the C projects arise from the high dimensionality, dynamics, and volume of the data, as are generated, for instance, by astro and particle physics. The overarching projects in the A domain encompass both other pillars.